A Socio-Technical Perspective on Urban Analytics: The Case of City-Scale Digital Twins

Figures & data

Figure 1. The Gemini Principles for built environment digital twins. Source: Bolton et al., 2018.

Figure 2. Purpose and framework for the Cambridge City-scale Digital Twin

Table 1. Summary of the interviews conducted with local actors

Type of Organization	Number of Interviews
Public sector (including councils and the Greater Cambridge Partnership)	14
Private sector (including employers, service providers and coworking spaces)	7
Community/third sector (including citizens’ activist groups, residents’ associations’, employees and self-employed)	6

Figure 3. Actor overview—Cambridge CDT ‘prospective users’

Table 2. Organizational fragmentation and siloed decision-making in Cambridge

Type of Fragmentation	Context Description	Impact
Vertical	Strategic powers (Combined Authority, County, City and District councils) and implementation (Greater Cambridge Partnership) separated. Varying political leadership on different levels of government (Conservative, Labour and Liberal Democrat parties).	Disconnect between the strategic plans and initiatives on the ground. Contradictory or incompatible aims being pursued by different local authorities. Discrepancies in terms of accountability in policy decision-making. Limited opportunities for citizens to contribute to decisions impacting their everyday lives.
Horizontal	Key activities (provision of modelling evidence; management and operation of urban infrastructures) influencing city planning and management contracted out to external partners. Emerging community groups aiming to influence policy decision-making for land-use planning and transport.	Lacking oversight (linking planning to operation/management) and in-depth knowledge (capability to evaluate modelling scenario outputs) among decision-makers. Due to the privatization and commercialization of ‘evidence production’, decision-makers and community groups have no access to data and urban models. Difficulties with communicating the use of modelling outputs (data-driven decision-making) in policy and implementation.
Sectoral	Siloed sectoral decision-making in land-use planning, transport, energy and air quality. Evidence provision (including data and modelling) remains largely locked into sectoral silos. Capturing local knowledge / community feedback limited to transport and land-use planning; engagement in the energy domain is lacking.	Contradictory or incompatible recommendations for policy decision-making (e.g., between transport and energy infrastructure planning and management). Potentially favoring certain sectors (land-use, transport) or prioritizing specific policies over others (energy) without adequately addressing problems situated at the nexus.

Sources: Nochta et al., 2019a; 2019b; 2019c.

Table 3. The role of modeling evidence in policy decision-making

Type of Concern	Context Description	Impact
Trust (validity and transparency)	Perceived inadequacy of data input (e.g. mismatch between official data sourced from national level and locally collected data; data availability obstructed by difficulties with accessing privately owned datasets). Multiple incompatible or contradictory decision options underpinned by—internally coherent—evidence based on modelling (e.g. developed in sectoral silos or for different governments with varying political leadership and priorities). Production of evidence (i.e., the modelling process itself) detached from its utilization in policy-making. Decision-makers only see ready-made interpretations of results—data inputs, modelling assumptions and associated uncertainties are insufficiently understood.	Contesting the value of evidence derived from modelling (questionable validity of outputs / recommendations; lack of transparency around provision). Limited options for developing more transparent modelling practices (commercial interests of the private sector; lacking skills and expertise in the public and third sectors). Limited options for evaluating, testing or scrutinizing results through other models or methods. Difficulties with contract commissioning, potentially leading to a duplication of modelling efforts and unnecessary expenditure. Resources required (time and cost) for modelling hinder a more dynamic use of models to test different scenarios internally before commissioning external modelling.
Legitimacy (democratic quality and oversight)	Opportunities for strategic oversight are limited due to a lack of capabilities to make sense of, and coordinate among, various modelling exercises. Engagement processes with local communities and individual citizens remain detached from data collection and modelling—disconnect among different types of evidence (e.g. professional experience, modelling outputs and citizen preferences). Strategic decisions are most often underpinned by modelling recommendations, consultation processes tend to be created later in the projects’ timeline. Community groups and individuals will continue to explore and utilize non-traditional channels to make their voices heard.	Difficulties with communicating evidence and its implications for decision options towards others—including senior officers, elected councilors and urban citizens. Sense of competition among different types of evidence for influence over policy decision-making, hindering constructive combination of different ways of ‘knowing’ the city into a more coherent evidence base for urban policy. Lacking citizen inclusion at the design stage of proposed interventions can prove to be at great monetary and time costs to deliver on development targets.

Sources: Nochta et al., 2019a; 2019b; 2019c.

Table 4. Overview of technical requirement for the Cambridge CDT according to local actors’ requirements

Type of Local Actor	Technical Requirements for Cambridge CDT
Local authorities and external partners	Must be usable: lightweight and user-friendly enough to be run in-house to explore questions, scenarios and for scrutiny (more dynamic use of models in decision-making). Recommendations must align with local powers, responsibilities and reporting requirements (towards national / supra-national levels). Must be useful: link strategic planning and tactical/operational management through predictions and monitoring. Must make use of existing modelling efforts (sunk investment, data collection costs and ensuring continuity).
Urban citizens	Must support the communication of data-driven decisions and policy making processes between the public and local authorities (transparency, accountability). Must provide a new channel for more frequent interaction, engagement and debate (democratic quality, oversight). Must support community-led initiatives, e.g., developing alternative scenarios in community setting, understand tradeoffs, scrutiny (democratic quality, oversight).

Figure 4. Conceptualizing the relationship between the CDT and existing sectoral models

Figure 5. Modeling framework for the Cambridge CDT prototype for journeys to work

Figure 6. Local and regional authorities in the Cambridge and Peterborough Combined Authority area

Figure 7. EV charging scenario: Alternative spatial development strategies for Cambridge and the environs

Figure 8. Model output of EV charging scenario I: Densification

Figure 9. Model output of EV charging scenario II: Urban Fringe